Triangulation of bayesian networks using an adaptive genetic algorithm

Authors:
Hao Wang;Kui Yu;Xindong Wu;Hongliang Yao
Affiliations:
Department of Computer Science and Technology, Hefei University of Technology, Hefei, China;Department of Computer Science and Technology, Hefei University of Technology, Hefei, China;Department of Computer Science and Technology, Hefei University of Technology, Hefei, China;Department of Computer Science and Technology, Hefei University of Technology, Hefei, China
Venue:
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Year:
2006

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Abstract

The search for an optimal node elimination sequence for the triangulation of Bayesian networks is an NP-hard problem. In this paper, a new method, called the TAGA algorithm, is proposed to search for the optimal node elimination sequence. TAGA adjusts the probabilities of crossover and mutation operators by itself, and provides an adaptive ranking-based selection operator that adjusts the pressure of selection according to the evolution of the population. Therefore the algorithm not only maintains the diversity of the population and avoids premature convergence, but also improves on-line and off-line performances. Experimental results show that the TAGA algorithm outperforms a simple genetic algorithm, an existing adaptive genetic algorithm, and simulated annealing on three Bayesian networks.